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CHI
2010
ACM

Examining multiple potential models in end-user interactive concept learning

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Examining multiple potential models in end-user interactive concept learning
End-user interactive concept learning is a technique for interacting with large unstructured datasets, requiring insights from both human-computer interaction and machine learning. This note re-examines an assumption implicit in prior interactive machine learning research, that interaction should focus on the question “what class is this object?”. We broaden interaction to include examination of multiple potential models while training a machine learning system. We evaluate this approach and find that people naturally adopt revision in the interactive machine learning process and that this improves the quality of their resulting models for difficult concepts. Author Keywords End-user interactive concept learning. ACM Classification Keywords H5.2 Information Interfaces and Presentation: User Interfaces.
Saleema Amershi, James Fogarty, Ashish Kapoor, Des
Added 17 May 2010
Updated 17 May 2010
Type Conference
Year 2010
Where CHI
Authors Saleema Amershi, James Fogarty, Ashish Kapoor, Desney S. Tan
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